The following table lists the monthly incomes (in hundreds of dollars) and the monthly rents paid (in hundreds of dollars) by a sample ofsix families. Let x be monthly income and y be monthly rent.Monthly Income Monthly Rent247.0164.5196.53111.6104.5278.5Using the regression of monthly rent paid on monthly income, the predicted value of monthly rent paid by a family with a monthlyincome of $3,000, rounded to the nearest dollar, is:

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Answered: The following table lists the monthly…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The tables show the amount of revenue (y) in billions for different companies x years after 2005. Match the table with the correct description of its regression model and correlation. Negative Linear Regression 1. a0 1 2 3 4 5 6 78 9 10 Strong Negative Correlation y 12 12 13 14 15 16 18 19 21 22 25 Positive Linear Regression * 0 1 2 3 4 5 6 7 89 10 y 20 18 17 15 13 14 16 18 21 25 28 2. Strong Positive Correlation Quadratic Regression * 0123 4 5 6 7 8 9 10 3. Strong Positive Correlation y 5679 13 15 18 24 25 28 30 Increasing Exponential Regression 4. x0 1 2 3 4 5 6 789 10 Strong Positive Correlation y 20 20 19 18 16 15 12 8765 Decreasing Exponential Regression z0 1 2 3 4 5 6 7 8 9 10 5. Strong Negative Correlation y 20 19 20 18 16 15 13 11 10 11 10
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D. Calculate a linear regression using Stabley. Write the equation below. E. What is the correlation coefficient? f. What is the slope of your LSRL? Interpret the slope using context. G. What is the Y- intercept of your LSRL? Interpret the Y-intercept using context
Calculate the residual for the first observation in the dataset.
The correlation between first year college GPA and high school GPA is 0.683. If a simple linear regression was conducted to predict first year college GPA from high school GPA what does the correlation tell us about the fit of the model?
It is inferred that the tip amount has a relationship with value of a dinner bill. The collected data is presented in the table. Conduct a regression analysis. X - Bill ($) 33.46 50.68 87.92 98.84 63.60 107.34 Y - Tip ($) 5.70 5.00 8.08 17.00 12.00 16.00
The following table gives the personal savings (in billions of dollars) in the United States for selected years. Year 2005 2010 2011 2012 2013 2014 2015 2016 Personal Savings (billions) $153.8 189.3 187.5 142.0 155.7 152.1 175.6 199.6 5. Find the average rate of change between the years 2010 and 2012. 6. Interpret questions 4 and 5 on the graph. 7. Find the point of inflection of the regression equation and interpret what it means.
A physician wishes to know whether there is a relationship between a father’s weight (in pounds) and his new born sons’ weight (in pounds). The data are given here Fathers Weight 176 145 187 210 196 142 205 215 152 145 Sons Weight 6.6 8.2 9.2 7.1 8.8 9.3 7.1 8.6 7.8 6.0 Develop the regression equation for the sample Predict the sons weight based on the father’s weight of 300, 250 and 400.
5. Explain what the correlation coefficient tells you about the relationship between your variables. 7. Interpret the slope of the regression equation in terms of the variables in your data set.
The personnel director of a large hospital is interested in determining the relationship (if any) between an employee's age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 Age 30 50 40 55 30 28 60 25 Sick Days 7 4 3 2 9 10 0 8 Copy Data The estimated regression line and the standard error are given. Sick Days = 14.310162 - 0.2369(Age 9 10 30 45 5 2 Se = 1.682207 Find the 95% confidence interval for the average number of sick days an employee will take per year, given the employee is 26. Round your answer to two decimal places.
The table below shows the age of 8 individuals and the packs of medicine they consume each year. Age 17 27 54 35 42 31 46 23 Medicine (in packs) 17 7 12 6 2 a. Determine the correlation coefficient r between the two variables. Answer rounded to at least 4 decimal places. b. Determine the slope coefficient B1 for the regression of medicine packs consumed on age. Answer rounded to at least 4 decimal places. c. Determine the y-intercept Bo for the regression of medicine packs consumed on age. Answer rounded to at least 4 decimal places. d. What will be the expected packs of medicines consumed per year by a person of age 33. Answer rounded to at least 4 decimal places. packs

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